Understanding Embryonic Robustness: Quantitative Experiments and Theory Project Summary The primary aim of this project is to understand how gene regulation generates precise spatial patterns in embryonic development. A major focus of developmental biology is to characterize how networks of genes and their products create distinct and spatially separated cell types. However, the chemical reactions and transport processes underlying pattern formation are subject to numerous sources of variability and noise. Extrinsic sources include variability in temperature, size and maternally-supplied factors. Intrinsic noise arises from the low concentrations of many biological molecules and the random aspects of cell shape, orientation and movement. For development to reliably form complex body plans, gene network dynamics must be robust to these disruptive influences. Investigating the generation and control of spatial noise requires a quantitative methodology. Choosing one of the genetically best characterized model systems for embryonic patterning, anterior-posterior segmentation in the fruit fly Drosophila, allows us to simplify the biological challenges, so that we can focus on noise characterization. Our ultimate goal, however, is to contribute to understanding, and perhaps limiting, human birth defects. Our work should also be directly relevant to the variable disease outcomes associated with incomplete gene penetrance, and to error control mechanisms for limiting cancer. Studies of noise and variability require careful quantitation, so a major focus of our work is development of robust image processing and statistical techniques for separating signal from different types of noise in whole embryo images. Variability between signals from different embryos provides data on the variability of global parameters;the different types of intrinsic noise provide data on within-embryo variation. We use modeling to understand how variability or noise arises in the segmentation gene network, and how they might be controlled. We model dynamics at the gene network and the promoter structure levels. With stochastic modeling at the latter level, we can test regulatory hypotheses by fitting to the noise and noise distributions of the intrinsic noise components from our data. Network level modeling allows us to test hypotheses on how patterns are made robust to gene recruitment and mutation, tested against patterns from wild-type and mutant embryos. Mathematical analysis is used to characterize the dynamics of these processes. We organize our research into 3 specific aims, to characterize noise and variability in: 1) Maternal gradients: we focus on unanswered questions regarding the temporal and sub-cellular patterning of these gradients. 2) Gap gene response: we quantify how maternal noise and variability affect embryonic expression, as well as how noise arises in the process of gene expression. This includes statistics showing the degree to which embryos reduce upstream variability, and modeling to characterize noise generation and control in gene regulation. 3) The gap gene ensemble: here we focus on the interactions between zygotic genes that provide pattern robustness to temperature or mutational disruptions.